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Artículos de revistas sobre el tema "Time-series analysis"

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Publicado: 4 de junio de 2021
Última modificación: 29 de julio de 2024

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1

Zhuravka, Fedir, Hanna Filatova, Petr Šuleř y Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis". Investment Management and Financial Innovations 18, n.º 1 (28 de enero de 2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.
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2

Lutsenko, V. V., N. N. Kucherov y A. V. Gladkov. "Predicting traffic congestion based on time series analysis". Sovremennaya nauka i innovatsii, n.º 2 (42) (2023): 50–58. http://dx.doi.org/10.37493/2307-910x.2023.2.5.

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Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion.
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3

Lutsenko, V. V., N. N. Kucherov y A. V. Gladkov. "PREDICTING TRAFFIC CONGESTION BASED ON TIME SERIES ANALYSIS". Sovremennaya nauka i innovatsii, n.º 1 (41) (2023): 47–55. http://dx.doi.org/10.37493/2307-910x.2023.1.4.

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Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion
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4

Bowerman, Bruce y Jonathan D. Cryer. "Time Series Analysis". Technometrics 29, n.º 2 (mayo de 1987): 240. http://dx.doi.org/10.2307/1269781.

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5

Donatelli, Richard E., Ji-Ae Park, Spencer M. Mathews y Shin-Jae Lee. "Time series analysis". American Journal of Orthodontics and Dentofacial Orthopedics 161, n.º 4 (abril de 2022): 605–8. http://dx.doi.org/10.1016/j.ajodo.2021.07.013.

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6

Potscher, Benedikt M. y James D. Hamilton. "Time Series Analysis." Journal of the American Statistical Association 91, n.º 433 (marzo de 1996): 439. http://dx.doi.org/10.2307/2291435.

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7

Bakouch, Hassan S. "Time Series Analysis". Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, n.º 1 (enero de 2009): 283. http://dx.doi.org/10.1111/j.1467-985x.2008.00571_4.x.

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8

Subba Rao, T. "Time Series Analysis". Journal of Time Series Analysis 31, n.º 2 (marzo de 2010): 139. http://dx.doi.org/10.1111/j.1467-9892.2009.00641.x.

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9

Breitung, Jorg y James D. Hamilton. "Time Series Analysis." Contemporary Sociology 24, n.º 2 (marzo de 1995): 271. http://dx.doi.org/10.2307/2076916.

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10

Taylor, Diana. "Time-Series Analysis". Western Journal of Nursing Research 12, n.º 2 (abril de 1990): 254–61. http://dx.doi.org/10.1177/019394599001200210.

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11

Mills, Terence C. "TIME SERIES ANALYSIS". Journal of Economic Surveys 9, n.º 3 (septiembre de 1995): 325–28. http://dx.doi.org/10.1111/j.1467-6419.1995.tb00120.x.

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12

Dattalo, Patrick. "Time Series Analysis". Journal of Community Practice 5, n.º 4 (30 de septiembre de 1998): 67–85. http://dx.doi.org/10.1300/j125v05n04_05.

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13

Bowermnn, Bruce. "Time Series Analysis". Technometrics 29, n.º 2 (mayo de 1987): 240. http://dx.doi.org/10.1080/00401706.1987.10488217.

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14

Booth, David E. "Time Series Analysis". Technometrics 39, n.º 1 (febrero de 1997): 102–3. http://dx.doi.org/10.1080/00401706.1997.10485448.

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15

Fildes, Robert. "Time series analysis". International Journal of Forecasting 2, n.º 1 (enero de 1986): 117. http://dx.doi.org/10.1016/0169-2070(86)90037-3.

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16

Marquez, Jamie. "Time series analysis". International Journal of Forecasting 11, n.º 3 (septiembre de 1995): 494–95. http://dx.doi.org/10.1016/0169-2070(95)90035-7.

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17

Gasser, T. "Time series analysis". Electroencephalography and Clinical Neurophysiology 61, n.º 3 (septiembre de 1985): S221. http://dx.doi.org/10.1016/0013-4694(85)90839-9.

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18

Watson, Mark W. y Jonathan D. Cryer. "Time Series Analysis." Journal of the American Statistical Association 82, n.º 400 (diciembre de 1987): 1195. http://dx.doi.org/10.2307/2289427.

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19

Militino, Ana F. "Time Series Analysis". Journal of Applied Statistics 36, n.º 6 (junio de 2009): 699–700. http://dx.doi.org/10.1080/02664760802366809.

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20

LEDERMAN, REGINA P. "Time Series Analysis". MCN, The American Journal of Maternal/Child Nursing 18, n.º 3 (mayo de 1993): 177. http://dx.doi.org/10.1097/00005721-199305000-00013.

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21

Raftery, Adrian E. "Time series analysis". European Journal of Operational Research 20, n.º 2 (mayo de 1985): 127–37. http://dx.doi.org/10.1016/0377-2217(85)90052-9.

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22

Tsujimoto, Kazuki y Toshiaki Omori. "Switching Probabilistic Slow Feature Analysis for Time Series Data". International Journal of Machine Learning and Computing 10, n.º 6 (diciembre de 2020): 740–45. http://dx.doi.org/10.18178/ijmlc.2020.10.6.999.

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23

Thinsungnoen, Tippaya, Kittisak Kerdprasop y Nittaya Kerdprasop. "A Deep Learning of Time Series for Efficient Analysis". International Journal of Future Computer and Communication 6, n.º 3 (septiembre de 2017): 123–27. http://dx.doi.org/10.18178/ijfcc.2017.6.3.503.

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24

Perea, Jose A. "Topological Time Series Analysis". Notices of the American Mathematical Society 66, n.º 05 (1 de mayo de 2019): 1. http://dx.doi.org/10.1090/noti1869.

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25

Geurts, Michael D. y Lois W. Sayrs. "Pooled Time Series Analysis". Journal of Marketing Research 27, n.º 4 (noviembre de 1990): 501. http://dx.doi.org/10.2307/3172639.

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26

Campbell, Katherine. "Nonlinear Time Series Analysis". Technometrics 43, n.º 4 (noviembre de 2001): 491. http://dx.doi.org/10.1198/tech.2001.s50.

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27

Liu, Zonghua. "Chaotic Time Series Analysis". Mathematical Problems in Engineering 2010 (2010): 1–31. http://dx.doi.org/10.1155/2010/720190.

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Chaotic dynamical systems are ubiquitous in nature and most of them does not have an explicit dynamical equation and can be only understood through the available time series. We here briefly review the basic concepts of time series and its analytic tools, such as dimension, Lyapunov exponent, Hilbert transform, and attractor reconstruction. Then we discuss its applications in a few fields such as the construction of differential equations, identification of synchronization and coupling direction, coherence resonance, and traffic data analysis in Internet.
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28

Ziegel, Eric R. "Applied Time Series Analysis". Technometrics 32, n.º 4 (noviembre de 1990): 451–52. http://dx.doi.org/10.1080/00401706.1990.10484736.

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29

Rycroft, Michael J. "Nonlinear Time Series Analysis". Journal of Atmospheric and Solar-Terrestrial Physics 62, n.º 2 (enero de 2000): 152. http://dx.doi.org/10.1016/s1364-6826(99)00123-6.

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30

Kosmidis, Kosmas, Alkiviadis Kalampokis y Panos Argyrakis. "Language time series analysis". Physica A: Statistical Mechanics and its Applications 370, n.º 2 (octubre de 2006): 808–16. http://dx.doi.org/10.1016/j.physa.2006.02.042.

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31

Bandt, Christoph. "Ordinal time series analysis". Ecological Modelling 182, n.º 3-4 (marzo de 2005): 229–38. http://dx.doi.org/10.1016/j.ecolmodel.2004.04.003.

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32

Grubb, Howard y Lois W. Sayrs. "Pooled Time Series Analysis." Statistician 39, n.º 1 (1990): 91. http://dx.doi.org/10.2307/2348206.

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33

Lasaponara, Rosa y Antonio Lanorte. "Satellite time-series analysis". International Journal of Remote Sensing 33, n.º 15 (13 de febrero de 2012): 4649–52. http://dx.doi.org/10.1080/01431161.2011.638342.

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34

Bakouch, Hassan S. "Applied time series analysis". Journal of Applied Statistics 39, n.º 10 (octubre de 2012): 2300–2301. http://dx.doi.org/10.1080/02664763.2012.682445.

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35

Foster, Grant. "Time Series Analysis by Projection. II. Tensor Methods for Time Series Analysis". Astronomical Journal 111 (enero de 1996): 555. http://dx.doi.org/10.1086/117806.

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36

Tu, Min, Yuanjian Zhang, Jianfeng Xu y Yu Li. "Analysis and Modeling of Time Series Based on Granular Computing". International Journal of Future Computer and Communication 4, n.º 2 (abril de 2015): 93–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.363.

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37

Delima, Allemar Jhone P. "Application of Time Series Analysis in Projecting Philippines’ Electric Consumption". International Journal of Machine Learning and Computing 9, n.º 5 (octubre de 2019): 694–99. http://dx.doi.org/10.18178/ijmlc.2019.9.5.860.

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38

Anupriya y Anita Singhrova. "Comparative Analysis of Time Series Forecasting Models for SDMN Traffic". Journal of Advanced Research in Dynamical and Control Systems 11, n.º 0009-SPECIAL ISSUE (25 de septiembre de 2019): 531–40. http://dx.doi.org/10.5373/jardcs/v11/20192602.

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39

Huang, Guangdong y Jiahong Li. "Hybrid Time Series Method for Long-Time Temperature Series Analysis". Discrete Dynamics in Nature and Society 2021 (23 de julio de 2021): 1–10. http://dx.doi.org/10.1155/2021/9968022.

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This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1967 to 2016 of northern mountainous area in North China as the observed data. Then, we use 10 different discrete wavelet functions to decompose and reconstruct the observed data. Next, we build ARMA models on all the reconstructed data. In the end, we regard the calculations of 10 DWT-ARMA (DA) algorithms and the observed data as the labels and target of the XGBoost algorithm, respectively. Through the data training and testing of the XGBoost algorithm, the optimal weights and the corresponding output of the hybrid DAX model can be calculated. Root mean squared error (RMSE) was followed as the criteria for judging the precision. This paper compared DAX with an equal-weighted average (EWA) algorithm and 10 DA algorithms. The result shows that the RMSE of the two hybrid algorithms is much lower than that of the DA algorithms. Moreover, the bigger decrease in RMSE of the DAX model than the EWA model represents that the proposed DAX model has significant superiority in combining models which proves that DAX has significant improvement in prediction as well.
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40

Lalley, Steven P. y A. B. Nobel. "Denoising deterministic time series". Dynamics of Partial Differential Equations 3, n.º 4 (2006): 259–79. http://dx.doi.org/10.4310/dpde.2006.v3.n4.a1.

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41

RAJASEKARAN, RAJKUMAR, GOVINDA K, ASHRITH REDDY, UDAY SAI REDDY y YASHWANTH REDDY. "Visual Analysis of Temperature Time Series and Rainfall Using Big Data". Journal of Research on the Lepidoptera 50, n.º 3 (25 de septiembre de 2019): 39–52. http://dx.doi.org/10.36872/lepi/v50i3/201023.

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42

Hanafiah, Mohd Hafiz Mohd y Mohd Fauzi Mohd Harun. "Tourism Demand in Malaysia: A cross-sectional pool time-series analysis". International Journal of Trade, Economics and Finance 1, n.º 1 (2010): 80–83. http://dx.doi.org/10.7763/ijtef.2010.v1.15.

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43

Hanafiah, Mohd Hafiz Mohd y Mohd Fauzi Mohd Harun. "Tourism Demand in Malaysia: A cross-sectional pool time-series analysis". International Journal of Trade, Economics and Finance 1, n.º 2 (2010): 200–203. http://dx.doi.org/10.7763/ijtef.2010.v1.36.

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44

Li, Ta-Hsin. "Time-correlation analysis of nonstationary time series". Journal of Time Series Analysis 19, n.º 1 (enero de 1998): 47–67. http://dx.doi.org/10.1111/1467-9892.00076.

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45

Nachane, D. M. "Time-Frequency Analysis for Nonstationary Time Series". Journal of Quantitative Economics 2, n.º 2 (julio de 2004): 41–57. http://dx.doi.org/10.1007/bf03404608.

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46

Drozdov, I. Yu y D. A. Petrusevich. "Water pollution time series analysis". IOP Conference Series: Materials Science and Engineering 1047, n.º 1 (1 de febrero de 2021): 012095. http://dx.doi.org/10.1088/1757-899x/1047/1/012095.

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47

Ziegel, Eric R. y T. S. Rao. "Developments in Time Series Analysis". Technometrics 37, n.º 1 (febrero de 1995): 127. http://dx.doi.org/10.2307/1269181.

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48

Zhang, Nien Fan, D. C. Montgomery, L. A. Johnson y J. S. Gardiner. "Forecasting and Time Series Analysis". Technometrics 34, n.º 1 (febrero de 1992): 98. http://dx.doi.org/10.2307/1269557.

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49

Ziegel, Eric R. y Chris Chatfield. "The Analysis of Time Series". Technometrics 39, n.º 3 (agosto de 1997): 342. http://dx.doi.org/10.2307/1271155.

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50

Valsakumar, M. C., K. P. N. Murthy y S. Venkadesan. "Time Series Analysis - A Review". Solid State Phenomena 42-43 (abril de 1995): 141–54. http://dx.doi.org/10.4028/www.scientific.net/ssp.42-43.141.

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